Role of microstructure in clay emanation

The influence of clay microstructure on radon emanation has been studied based on the analysis of literary data and direct measurements of the emanation coefficient of different types of clay rocks. Experiments have shown that modern and Holocene clay and silt with a cellular microstructure have the highest emanation coefficient (more than 60%). The majority of clay rocks transformed by diagenesis (showing either matrix or turbulent microstructure), have the emanation coefficient of about 50%. The emanation coefficients of lithified clay and argillite (laminar microstructure) do not exceed 10%. Thus, the emanation capacity of clay depends on clay microstructure and regularly decreases in the following row: cellular > matrix or turbulent > laminar microstructure.     

Landslide investigations in Russia and the former USSR

The impact of natural hazards on mankind has increased dramatically over the past decades. Global urbanization processes and increasing spatial concentrations of exposed elements induce natural hazard risk at a uniquely high level. To mitigate affiliated perils requires detailed knowledge about elements at risk. Considering a high spatiotemporal variability of elements at risk, detailed information is costly in terms of both time and economic resources and therefore often incomplete, aggregated, or outdated. To alleviate these restrictions, the availability of very-high-resolution satellite images promotes accurate and detailed analysis of exposure over various spatial scales with large-area coverage. In the past, valuable approaches were proposed; however, the design of information extraction procedures with a high level of automatization remains challenging. In this paper, we uniquely combine remote sensing data and volunteered geographic information from the OpenStreetMap project (OSM) (i.e., freely accessible geospatial information compiled by volunteers) for a highly automated estimation of crucial exposure components (i.e., number of buildings and population) with a high level of spatial detail. To this purpose, we first obtain labeled training segments from the OSM data in conjunction with the satellite imagery. This allows for learning a supervised algorithmic model (i.e., rotation forest) in order to extract relevant thematic classes of land use/land cover (LULC) from the satellite imagery. Extracted information is jointly deployed with information from the OSM data to estimate the number of buildings with regression techniques (i.e., a multi-linear model from ordinary least-square optimization and a nonlinear support vector regression model are considered). Analogously, urban LULC information is used in conjunction with OSM data to spatially disaggregate population information. Experimental results were obtained for the city of Valparaíso in Chile. Thereby, we demonstrate the relevance of the approaches by estimating number of affected buildings and population referring to a historical tsunami event.

     

The Coulomb–Mohr Law and the Change in the Stress–Strain State of a Landslide Prone Mass

Doklady Earth Sciences - The stresses at the points of the ground mass are determined and distributed according to the Coulomb–Mohr law in the main stresses, under compression conditions. The...